1 00:00:05,480 --> 00:00:08,600 Speaker 1: Kiyota. I'm Chelsea Daniels and this is the Front Page, 2 00:00:09,039 --> 00:00:16,360 Speaker 1: a daily podcast presented by the New Zealand Herald. New 3 00:00:16,440 --> 00:00:20,040 Speaker 1: Zealand's fight to attract the minds shaping the future in 4 00:00:20,200 --> 00:00:24,079 Speaker 1: tech could have just gotten a little bit harder. China 5 00:00:24,120 --> 00:00:27,520 Speaker 1: has a new visa which targets young STEM grads and 6 00:00:27,680 --> 00:00:31,920 Speaker 1: foreign tech talent. It's while a similar US visa now 7 00:00:32,000 --> 00:00:36,720 Speaker 1: comes with a one hundred thousand dollars fee attached. The 8 00:00:36,760 --> 00:00:41,160 Speaker 1: move has been described as boosting Beijing's fortunes in its 9 00:00:41,240 --> 00:00:45,680 Speaker 1: geopolitical rivalry with Washington. Today, on the front page, Victoria 10 00:00:45,840 --> 00:00:51,000 Speaker 1: University's senior lecturer in Artificial Intelligence Dtor Andrew Lenson is 11 00:00:51,040 --> 00:00:53,800 Speaker 1: with us to take a look at what makes this 12 00:00:53,920 --> 00:01:03,000 Speaker 1: country attractive and what might be holding us back. So, Andrew, 13 00:01:03,120 --> 00:01:08,480 Speaker 1: this new Chinese visa targets young foreign science, tech, engineering 14 00:01:08,560 --> 00:01:11,880 Speaker 1: and math graduates and promises to allow entry residents and 15 00:01:12,000 --> 00:01:16,280 Speaker 1: employment without a job offer. Would this be appealing to 16 00:01:16,440 --> 00:01:19,080 Speaker 1: the world's greatest young minds? Would you say? 17 00:01:19,360 --> 00:01:21,959 Speaker 2: Potentially? I mean, I think it does depend a little 18 00:01:22,000 --> 00:01:25,039 Speaker 2: bit on people's ambitions in life and some of their 19 00:01:25,040 --> 00:01:28,600 Speaker 2: world views. But we are seeing a lot of young, 20 00:01:28,880 --> 00:01:30,959 Speaker 2: bright minds who don't necessarily want to go to the 21 00:01:31,040 --> 00:01:34,160 Speaker 2: US because of what is happening there, but they still 22 00:01:34,200 --> 00:01:37,000 Speaker 2: want to be part of this really big sort of 23 00:01:37,040 --> 00:01:39,959 Speaker 2: technology revolution, right, And so somebody like China, who probably 24 00:01:40,000 --> 00:01:44,120 Speaker 2: is the second closest in terms of the opportunities in 25 00:01:44,160 --> 00:01:46,240 Speaker 2: that tech space, may be appealing to some. But as 26 00:01:46,240 --> 00:01:48,240 Speaker 2: we know, there's also a lot of people who don't 27 00:01:48,240 --> 00:01:50,520 Speaker 2: always agree with Chinese views or they are coach of things, 28 00:01:50,560 --> 00:01:53,560 Speaker 2: and so it might attract some, but I'm not sure 29 00:01:53,560 --> 00:01:56,360 Speaker 2: how impactful it will be in terms of, for example, 30 00:01:56,400 --> 00:01:57,360 Speaker 2: people from New Zealand. 31 00:01:58,200 --> 00:02:00,320 Speaker 1: Yeah, I mean some say that this is a sold 32 00:02:00,400 --> 00:02:03,800 Speaker 1: of changes in the US and how their visa system works. 33 00:02:04,280 --> 00:02:08,040 Speaker 1: How many of these young people dream of making it 34 00:02:08,080 --> 00:02:12,160 Speaker 1: to Silicon Valley. Is Silicon Valley the Hollywood for the 35 00:02:12,200 --> 00:02:12,799 Speaker 1: tech world? 36 00:02:13,200 --> 00:02:15,680 Speaker 2: Yeah, so it difinitely used to be. If you think 37 00:02:16,040 --> 00:02:18,720 Speaker 2: ten or fifteen years ago, right, everyone in my courses 38 00:02:18,720 --> 00:02:20,640 Speaker 2: would be thinking about going to Google or or going 39 00:02:20,720 --> 00:02:23,920 Speaker 2: to Facebook. And I think for a lot of people 40 00:02:23,960 --> 00:02:25,639 Speaker 2: it still is that sort of holy grail. If you're 41 00:02:25,639 --> 00:02:28,200 Speaker 2: really into the technical research side, you want to make 42 00:02:28,200 --> 00:02:30,800 Speaker 2: these really big, large beggers models and things. But then 43 00:02:30,840 --> 00:02:32,280 Speaker 2: there's also a lot of people who are looking at 44 00:02:32,320 --> 00:02:34,079 Speaker 2: that and going, actually, no, that isn't really a line 45 00:02:34,080 --> 00:02:36,680 Speaker 2: of my values anymore. I don't really feel good about 46 00:02:36,720 --> 00:02:38,959 Speaker 2: what's going on in the US or in the big 47 00:02:39,000 --> 00:02:42,000 Speaker 2: tech companies because they see the impact on the environment 48 00:02:42,160 --> 00:02:46,360 Speaker 2: on social systems politically, and so it's appearing still for 49 00:02:46,440 --> 00:02:50,080 Speaker 2: some people who are certain, I guess drive, but not 50 00:02:50,520 --> 00:02:52,280 Speaker 2: necessarily as wide square as it used to be. 51 00:02:53,120 --> 00:02:56,079 Speaker 1: Is a gen z more conscious about the world around 52 00:02:56,120 --> 00:02:59,760 Speaker 1: them perhaps than us millennials or any other generation. 53 00:02:59,440 --> 00:03:03,160 Speaker 2: Were, I think definitely. I mean when I talk to 54 00:03:03,160 --> 00:03:06,560 Speaker 2: students about AI, right, a lot of them have objections 55 00:03:06,560 --> 00:03:09,960 Speaker 2: to how it's used, how these big companies have sort 56 00:03:09,960 --> 00:03:13,400 Speaker 2: of forced it on us, and how it's been sort 57 00:03:13,440 --> 00:03:16,640 Speaker 2: of deployed about looking at these environmental costs, these social costs, 58 00:03:16,639 --> 00:03:18,160 Speaker 2: and so I think they are a lot more aware 59 00:03:18,680 --> 00:03:21,560 Speaker 2: or put a lot more credibility towards those parts of 60 00:03:21,600 --> 00:03:22,080 Speaker 2: the equation. 61 00:03:22,160 --> 00:03:26,320 Speaker 1: And perhaps we used to Is New Zealand doing enough 62 00:03:26,400 --> 00:03:28,800 Speaker 1: to attract this similar talent here. 63 00:03:29,440 --> 00:03:33,200 Speaker 2: That's a hard question because anyone will tell you we're small, 64 00:03:33,280 --> 00:03:36,520 Speaker 2: and so it's very hard for us to compete in 65 00:03:36,640 --> 00:03:38,880 Speaker 2: terms of developing some of these products we're not going 66 00:03:38,920 --> 00:03:42,400 Speaker 2: to make our own chat GBT, but at the same time, 67 00:03:42,960 --> 00:03:45,080 Speaker 2: we could really do some cool stuff in terms of 68 00:03:45,200 --> 00:03:48,160 Speaker 2: making AI trustworthy and showing the world how to do 69 00:03:48,200 --> 00:03:49,840 Speaker 2: this the right way and how to do it in 70 00:03:49,880 --> 00:03:52,400 Speaker 2: a way that puts people first. And I think that 71 00:03:52,440 --> 00:03:54,440 Speaker 2: is sort of the opportunity we have as one of 72 00:03:54,440 --> 00:03:57,160 Speaker 2: those moral leaders, and so on that basis, I think 73 00:03:57,240 --> 00:04:00,800 Speaker 2: resptively could be investing more both financially in terms of 74 00:04:00,800 --> 00:04:03,760 Speaker 2: attracting talent from overseas, but also in terms of hk 75 00:04:04,120 --> 00:04:07,520 Speaker 2: our own population, right, bringing up people from high school 76 00:04:07,640 --> 00:04:10,320 Speaker 2: university with those skills, and investing in education so that 77 00:04:10,360 --> 00:04:13,240 Speaker 2: we had the homegrown talent as well, because I think 78 00:04:13,560 --> 00:04:15,320 Speaker 2: we're never going to be able to offer those salaries 79 00:04:15,360 --> 00:04:17,440 Speaker 2: that let you see, you know, the moons and dollars 80 00:04:17,480 --> 00:04:20,360 Speaker 2: you see elsewhere, but we're can off sort of a lifestyle, 81 00:04:20,720 --> 00:04:22,839 Speaker 2: perhaps in a way of doing things that is more 82 00:04:22,960 --> 00:04:24,640 Speaker 2: aligned with views that people might hold. 83 00:04:24,760 --> 00:04:26,960 Speaker 1: Well, that's a really good point actually, because I read 84 00:04:27,040 --> 00:04:29,920 Speaker 1: as well that along you know, along with this China 85 00:04:30,400 --> 00:04:35,200 Speaker 1: visa opportunity, they've also done things like, for instance, home 86 00:04:35,279 --> 00:04:40,400 Speaker 1: purchase subsidies, signing bonuses of up to five million yuhan 87 00:04:40,800 --> 00:04:44,599 Speaker 1: or one point two million dollars. I mean, how how 88 00:04:44,640 --> 00:04:47,679 Speaker 1: do we compete with that? And you're saying, well, we could. 89 00:04:47,760 --> 00:04:55,400 Speaker 1: We could really cement ourselves as the morally ethically well 90 00:04:55,839 --> 00:04:59,400 Speaker 1: based Silicon Valley, right, Yeah, I mean. 91 00:04:59,240 --> 00:05:02,080 Speaker 2: And that's what we've done similar things in the world before, 92 00:05:02,360 --> 00:05:05,080 Speaker 2: when there was a nuclear free movement, or when we 93 00:05:05,480 --> 00:05:08,320 Speaker 2: gave universal suffrage too so that woman could vote, or 94 00:05:08,360 --> 00:05:10,719 Speaker 2: even some of the stuff with the christ it's called like, 95 00:05:10,760 --> 00:05:14,719 Speaker 2: we've made those headlines and those impacts beyond our scale before, right, 96 00:05:14,760 --> 00:05:17,440 Speaker 2: And so if we were purposeful about it, we too 97 00:05:17,520 --> 00:05:19,440 Speaker 2: could say, hey, this is how we want to do 98 00:05:19,480 --> 00:05:21,800 Speaker 2: AI in New Zealand. And I think that is also 99 00:05:22,440 --> 00:05:24,920 Speaker 2: not just the right thing to do, perhaps in many 100 00:05:24,920 --> 00:05:28,320 Speaker 2: people's views, but also an opportunity to set ourselves apart 101 00:05:28,600 --> 00:05:31,320 Speaker 2: economically as a provider of AI products and as a 102 00:05:31,360 --> 00:05:34,240 Speaker 2: trust we're replaced to do AI and to get AI 103 00:05:34,320 --> 00:05:37,240 Speaker 2: services from because we have if we had the things 104 00:05:37,240 --> 00:05:39,240 Speaker 2: in place to enable that. And so I think really 105 00:05:39,240 --> 00:05:41,280 Speaker 2: it's actually a gap in the market as well as 106 00:05:42,000 --> 00:05:43,920 Speaker 2: an appealing prospect for a lot of younger people. 107 00:05:44,040 --> 00:05:45,320 Speaker 1: How do we make that happen? 108 00:05:46,120 --> 00:05:50,200 Speaker 2: Yeah? So I think quite a few things. Of course, 109 00:05:50,240 --> 00:05:53,080 Speaker 2: there was that financial piece, so we would need to 110 00:05:53,120 --> 00:05:56,920 Speaker 2: invest in bringing talent here. We also need to invest 111 00:05:56,920 --> 00:05:59,520 Speaker 2: in education, so we had a funding of a tertiary 112 00:06:00,080 --> 00:06:04,839 Speaker 2: secondary school education systems, not just AI, but the rider sector. 113 00:06:04,920 --> 00:06:08,000 Speaker 2: So thinking about you know, the humanity, social sciences, because 114 00:06:08,000 --> 00:06:10,000 Speaker 2: all of those topics are really important as well when 115 00:06:10,040 --> 00:06:11,760 Speaker 2: we think about how to do this the right way, 116 00:06:11,880 --> 00:06:14,960 Speaker 2: but also looking at how we best regulate and best 117 00:06:15,279 --> 00:06:18,120 Speaker 2: put guard rails and manage this technology. Not again not 118 00:06:18,160 --> 00:06:22,240 Speaker 2: a stifle innovation, but to provide those certainties and those 119 00:06:22,279 --> 00:06:26,000 Speaker 2: sort of rules in place so that people, both our 120 00:06:26,040 --> 00:06:28,240 Speaker 2: own citizens as well a people overseas are trusting that 121 00:06:28,279 --> 00:06:30,400 Speaker 2: we're doing things in a good way and that there 122 00:06:30,400 --> 00:06:32,279 Speaker 2: are appropriate things in place. So I guess there's that 123 00:06:32,600 --> 00:06:34,840 Speaker 2: financial part, there's a education part, and then also sort 124 00:06:34,880 --> 00:06:36,080 Speaker 2: of a regulatory piece of work. 125 00:06:43,120 --> 00:06:48,000 Speaker 3: Personally, yeah, I've started using chat juputent, usually for research reasons, 126 00:06:48,040 --> 00:06:49,520 Speaker 3: like you know, like I might be interested in a 127 00:06:49,560 --> 00:06:51,919 Speaker 3: topic or a piece of history or something like that, 128 00:06:51,960 --> 00:06:53,600 Speaker 3: and I want to sort of get a quick distillation 129 00:06:53,680 --> 00:06:59,000 Speaker 3: of of something more for personal use. But with respect 130 00:06:59,000 --> 00:07:01,080 Speaker 3: to government, I mean, we really think AI is a 131 00:07:01,080 --> 00:07:04,200 Speaker 3: massive opportunity for Ye Zealand. One of the real challenges 132 00:07:04,200 --> 00:07:06,680 Speaker 3: we have is that we've not been We're all working 133 00:07:06,720 --> 00:07:08,839 Speaker 3: really hard in this country, but we haven't been able 134 00:07:08,839 --> 00:07:11,280 Speaker 3: to lift our standard of living over the last thirty years. 135 00:07:11,600 --> 00:07:13,360 Speaker 3: And a big reason for that. One of the big 136 00:07:13,360 --> 00:07:17,120 Speaker 3: contributing factors is we're not embracing enough technology innovation, and 137 00:07:17,400 --> 00:07:20,679 Speaker 3: certainly AI comes into their big time, because that's ultimately 138 00:07:20,680 --> 00:07:23,360 Speaker 3: how government will get much more efficient. It's ulterately how 139 00:07:23,360 --> 00:07:25,160 Speaker 3: our businesses will get more efficient as well. 140 00:07:26,840 --> 00:07:29,760 Speaker 1: When it comes to that education part, our kids today 141 00:07:30,280 --> 00:07:33,840 Speaker 1: taught well a are they taught anything about AI in 142 00:07:34,280 --> 00:07:37,680 Speaker 1: say high school, primary school or do they really have 143 00:07:37,760 --> 00:07:42,280 Speaker 1: to leave high school, you know, really interested in STEM 144 00:07:42,520 --> 00:07:45,640 Speaker 1: and then choose to do that maybe in higher education. 145 00:07:46,680 --> 00:07:50,880 Speaker 2: Yeah, So we saw the Ministry of Education did announce 146 00:07:51,120 --> 00:07:54,560 Speaker 2: some work going forward to have AI as part of 147 00:07:54,560 --> 00:07:57,880 Speaker 2: the curriculum, and so that's a good I'm not sure 148 00:07:57,880 --> 00:07:59,920 Speaker 2: what it's going to look like exactly, but it looks 149 00:08:00,080 --> 00:08:02,720 Speaker 2: like there is some progress being made there. But at 150 00:08:02,760 --> 00:08:04,480 Speaker 2: least at the moment, very much is the sort of 151 00:08:04,560 --> 00:08:08,960 Speaker 2: self driven thing. Some digital technology teachers are putting it 152 00:08:09,000 --> 00:08:12,120 Speaker 2: more and more into their year twelve year through ing content, 153 00:08:12,240 --> 00:08:15,640 Speaker 2: but that often is driven by their own themselves, right, 154 00:08:15,680 --> 00:08:17,680 Speaker 2: It's not necessarily that they have the support to do that, 155 00:08:17,800 --> 00:08:19,600 Speaker 2: because that sort of thing is a lot of these 156 00:08:19,640 --> 00:08:23,720 Speaker 2: teachers haven't necessarily been trained in technology. They've been trained 157 00:08:23,720 --> 00:08:26,240 Speaker 2: in other areas and sort of been asked to teach 158 00:08:26,280 --> 00:08:29,000 Speaker 2: these courses because there's not anyone available. And so there's 159 00:08:29,000 --> 00:08:31,560 Speaker 2: also need to upskill people at high schools to be 160 00:08:31,600 --> 00:08:35,040 Speaker 2: able to deliver the education at that level. But also 161 00:08:35,080 --> 00:08:37,240 Speaker 2: we see when students come to university that often they 162 00:08:37,280 --> 00:08:39,600 Speaker 2: can be quite interested in it, and so we have 163 00:08:40,320 --> 00:08:43,000 Speaker 2: a first year AI course that is genuine entry. Anyone 164 00:08:43,000 --> 00:08:44,880 Speaker 2: can do it, and that's a really nice course because 165 00:08:44,880 --> 00:08:46,120 Speaker 2: they can sort of get a taste for it, and 166 00:08:46,160 --> 00:08:49,320 Speaker 2: then even if they don't end up during an AI degree, 167 00:08:49,360 --> 00:08:51,400 Speaker 2: they still have more knowledge about it. And again it's 168 00:08:51,440 --> 00:08:54,679 Speaker 2: about building that broader capability so that we have these 169 00:08:54,840 --> 00:08:56,560 Speaker 2: just understanding these conversations as. 170 00:08:56,480 --> 00:08:58,680 Speaker 1: Well, and I guess just realizing that there is a 171 00:08:58,800 --> 00:09:02,280 Speaker 1: vast spectrum of job opportunities in the AI space. You 172 00:09:02,320 --> 00:09:05,480 Speaker 1: don't just become, oh, I'm an AI engineer. Now, like 173 00:09:05,520 --> 00:09:09,280 Speaker 1: there are specifics involved, right, There are different avenues that 174 00:09:09,320 --> 00:09:10,400 Speaker 1: you can don't go down. 175 00:09:11,240 --> 00:09:13,880 Speaker 2: Yeah, definitely, And something we're seeing a lot more of 176 00:09:13,920 --> 00:09:17,240 Speaker 2: as well is a demand not just for knowing about AI, 177 00:09:18,000 --> 00:09:22,080 Speaker 2: but also knowing about some other area of science, of 178 00:09:22,920 --> 00:09:25,920 Speaker 2: or of business. Right, so people who can understand AI 179 00:09:26,000 --> 00:09:28,120 Speaker 2: and how to apply it in a financial sector, or 180 00:09:28,280 --> 00:09:31,640 Speaker 2: understand AI and how to use it in healthcare. Often 181 00:09:31,640 --> 00:09:34,240 Speaker 2: it's having those joint sets of skills because it's not 182 00:09:34,320 --> 00:09:36,679 Speaker 2: as simple as just take your AI and plug it in. 183 00:09:36,960 --> 00:09:40,000 Speaker 2: You need to understand the problem, the data, the ethical 184 00:09:40,000 --> 00:09:42,880 Speaker 2: and moral issues as well. And so I think, as 185 00:09:42,880 --> 00:09:44,520 Speaker 2: you said, there's a lot of opportunities out there, and 186 00:09:44,559 --> 00:09:47,800 Speaker 2: I really encourage students to sort of take the most 187 00:09:47,840 --> 00:09:50,600 Speaker 2: they can at a university and get that broader diversity 188 00:09:50,760 --> 00:09:51,800 Speaker 2: of skill sets as well. 189 00:09:52,240 --> 00:09:54,200 Speaker 1: Have you seen your class size growing? 190 00:09:55,520 --> 00:09:58,160 Speaker 2: Yeah, so our first year class is at about two 191 00:09:58,280 --> 00:10:03,520 Speaker 2: hundred and fifty students this year. Yeah, it's busy. And 192 00:10:03,559 --> 00:10:05,440 Speaker 2: when we first offered it two years ago it was 193 00:10:05,480 --> 00:10:08,280 Speaker 2: about one hundred and thirty one hundred and forty, so 194 00:10:08,280 --> 00:10:12,280 Speaker 2: it's gone quite a bit. And our AI major, which 195 00:10:12,360 --> 00:10:14,640 Speaker 2: we're the first university to offer that in New Zealand, 196 00:10:15,520 --> 00:10:17,720 Speaker 2: has also gone a lot bigger. We have about sixty 197 00:10:17,800 --> 00:10:21,360 Speaker 2: or seventy students taking that through the whole program year 198 00:10:21,360 --> 00:10:23,840 Speaker 2: by year, and so there's certainly an uptake. I think 199 00:10:23,880 --> 00:10:26,240 Speaker 2: it's one of our faster growing majors. And of course 200 00:10:26,280 --> 00:10:30,000 Speaker 2: I'm excited about that. Yeah, it's good. 201 00:10:30,280 --> 00:10:33,120 Speaker 1: Well, you're in for a job for the foreseeable future, 202 00:10:33,240 --> 00:10:36,520 Speaker 1: and I mean I suppose I suppose these kids know 203 00:10:36,600 --> 00:10:38,320 Speaker 1: that as well, because when we look at the future 204 00:10:38,400 --> 00:10:41,640 Speaker 1: job market, I think that everyone is going to have 205 00:10:41,720 --> 00:10:44,320 Speaker 1: to know how at least how to use AI on 206 00:10:44,360 --> 00:10:47,960 Speaker 1: a basic level, just like everyone had to use, you know, 207 00:10:48,240 --> 00:10:49,520 Speaker 1: learn how to use touch phones. 208 00:10:49,880 --> 00:10:53,200 Speaker 2: Yeah, yeah, I think so. And I think it's not 209 00:10:53,240 --> 00:10:54,920 Speaker 2: just being able to use those tools, right, It's not 210 00:10:54,960 --> 00:10:57,240 Speaker 2: just being able to use co pilot or chat GBT. 211 00:10:57,760 --> 00:11:01,600 Speaker 2: It's also about understanding enough about how they work under 212 00:11:01,600 --> 00:11:04,679 Speaker 2: the hood to know their limitations and their issues and things. 213 00:11:04,679 --> 00:11:07,240 Speaker 2: Because again, that is where we see a lot of 214 00:11:07,240 --> 00:11:10,839 Speaker 2: the problems crop up, is when people misunderstand how these 215 00:11:10,880 --> 00:11:13,640 Speaker 2: models work, or they try and you ask it for 216 00:11:14,120 --> 00:11:16,600 Speaker 2: an answer to something that did wrong, and once you 217 00:11:16,640 --> 00:11:18,840 Speaker 2: know a bit about technology, you start to know why 218 00:11:18,920 --> 00:11:22,280 Speaker 2: that isn't quite effective. And so yeah, I think it's 219 00:11:22,320 --> 00:11:24,000 Speaker 2: going to be a really important skill set, and even 220 00:11:24,040 --> 00:11:26,560 Speaker 2: just doing one or two courses can really position you 221 00:11:27,120 --> 00:11:30,680 Speaker 2: as a much more capable person going into the workforce. Yeah. 222 00:11:30,920 --> 00:11:33,600 Speaker 1: And given how the world is going and how it's 223 00:11:33,640 --> 00:11:36,720 Speaker 1: progressing towards things like AI, do you think that the 224 00:11:36,800 --> 00:11:39,840 Speaker 1: tech and AI sector in New Zealand is well funded 225 00:11:39,880 --> 00:11:40,440 Speaker 1: at the moment? 226 00:11:41,720 --> 00:11:42,600 Speaker 2: No, of course not. 227 00:11:43,840 --> 00:11:45,400 Speaker 1: I was hoping you were going to say that. 228 00:11:45,679 --> 00:11:49,920 Speaker 2: Yeah. I mean, look, our government has kind of cut 229 00:11:49,960 --> 00:11:52,720 Speaker 2: funding across all the sciences, and even though they are 230 00:11:52,760 --> 00:11:55,280 Speaker 2: sort of advocating more money for AI re to sort 231 00:11:55,280 --> 00:11:57,800 Speaker 2: of see what that will look like. There was an 232 00:11:57,840 --> 00:12:00,720 Speaker 2: announcement of seventy million dollars, but that's kind of old 233 00:12:00,720 --> 00:12:03,800 Speaker 2: funding for something else being reused over here, and so 234 00:12:04,000 --> 00:12:07,439 Speaker 2: it's not the investment that I would want to see again, 235 00:12:07,480 --> 00:12:10,719 Speaker 2: both in terms of supporting small medium businesses as well 236 00:12:10,720 --> 00:12:14,760 Speaker 2: as education sector and even research funding. Like as an 237 00:12:14,800 --> 00:12:17,760 Speaker 2: AI researcher, I still have to compete to get funding 238 00:12:17,800 --> 00:12:20,240 Speaker 2: for things as I should, but there's not this massive 239 00:12:20,320 --> 00:12:23,640 Speaker 2: investment in funding to allow us to explore these issues 240 00:12:23,679 --> 00:12:26,320 Speaker 2: in AI or to talk about these and understand how 241 00:12:26,320 --> 00:12:27,800 Speaker 2: it impacts New Zealand. 242 00:12:27,600 --> 00:12:30,720 Speaker 1: And the importance on keeping those social sciences as well. 243 00:12:31,040 --> 00:12:33,160 Speaker 1: Is not only do we need people plugging in and 244 00:12:33,600 --> 00:12:36,839 Speaker 1: you know, making AI, I don't know what the terms are, 245 00:12:37,840 --> 00:12:40,680 Speaker 1: but we need to understand how it affects our life 246 00:12:40,679 --> 00:12:42,080 Speaker 1: and our society as well. 247 00:12:42,840 --> 00:12:46,239 Speaker 2: Yeah, and we saw that the government cut the Humanities 248 00:12:46,280 --> 00:12:48,600 Speaker 2: panel from the Mardson Fund this year, and that's one 249 00:12:48,600 --> 00:12:51,520 Speaker 2: of the biggest sort of blue sky what we call 250 00:12:51,600 --> 00:12:54,640 Speaker 2: blue sky research funds, which is research that is sort 251 00:12:54,640 --> 00:12:56,599 Speaker 2: of know, very forward looking and looking at some of 252 00:12:56,679 --> 00:12:58,880 Speaker 2: these bigger issues. And they're kind of funding for that 253 00:12:58,920 --> 00:13:01,800 Speaker 2: because they're more focus on this sort of economic growth model, 254 00:13:02,240 --> 00:13:04,280 Speaker 2: but need a lot of myself and my colleagues who 255 00:13:04,280 --> 00:13:06,760 Speaker 2: are AI people. So no, no, you can't do that. 256 00:13:06,760 --> 00:13:09,640 Speaker 2: We need the humanities now, right, And so even if 257 00:13:09,679 --> 00:13:13,480 Speaker 2: you don't believe that humanity is important before, which is 258 00:13:13,559 --> 00:13:16,640 Speaker 2: questionable at least with AI being in prison, you should 259 00:13:16,720 --> 00:13:18,800 Speaker 2: really see that it's important to fund there and have 260 00:13:19,280 --> 00:13:22,120 Speaker 2: social science research about how we use the technology and 261 00:13:22,120 --> 00:13:24,280 Speaker 2: how it impacts us as a society. 262 00:13:24,520 --> 00:13:26,559 Speaker 1: Thank you so much for talking with us, Andrew. 263 00:13:27,080 --> 00:13:27,400 Speaker 2: Thank you. 264 00:13:30,960 --> 00:13:34,200 Speaker 1: That's it for this episode of the Front Page. You 265 00:13:34,240 --> 00:13:38,120 Speaker 1: can read more about today's stories and extensive news coverage 266 00:13:38,160 --> 00:13:42,240 Speaker 1: at nzdherld dot co dot nz. The Front Page is 267 00:13:42,320 --> 00:13:45,720 Speaker 1: produced by Jane Ye and Richard Martin, who is also 268 00:13:45,920 --> 00:13:50,400 Speaker 1: our editor. I'm Chelsea Daniels. Subscribe to the Front Page 269 00:13:50,440 --> 00:13:54,080 Speaker 1: on iHeartRadio or wherever you get your podcasts, and tune 270 00:13:54,080 --> 00:13:57,120 Speaker 1: in tomorrow for another look behind the headlines.